Microservices That Actually Help
We don't cut monoliths into fifty services because a conference talk said so. We build microservice architectures where the split lines up with the team lines, the data lives in the right place, and the complexity you take on is paid back with real independence — with service meshes, observability, and a rollback story written before launch.
Microservices aren't the goal.
Independent teams shipping safely are.
Most microservice projects fail the same way: a monolith gets chopped up before anyone has mapped the domain, every new service re-implements auth and logging from scratch, the database stays shared, the deploys stay coupled, and now there are thirty things to debug instead of one. We start from the team structure and the domain, not the org chart of a FAANG blog post.
Premature extraction
A two-person team with a three-month-old product splits into eight services. Now every feature touches four repos, two teams, and a release train nobody controls.
Distributed monoliths
Services that can only deploy together, share a database, and fail in lockstep. All the pain of microservices, none of the independence.
Service mesh complexity theater
Istio installed, dashboards pretty, nobody on the team can explain why. The mesh adds more operational load than the problem it was meant to solve.
No clear ownership
Service X was written by a contractor in 2022. Three teams touch it, none own it. When it breaks at 3am, the on-call rotation is a Slack argument.
Every service re-inventing auth
JWT parsing copy-pasted six ways, rate limits inconsistent, audit logs missing in half the services. Security posture is whatever the last engineer had time for.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
A domain map that justifies the split
Bounded contexts, aggregates, and service boundaries drawn from how the business actually works — with a written argument for every split and every thing we left together.
A platform, not just services
Shared libraries for auth, logging, config, tracing, retries. A service template so the next service ships in a day. You get leverage, not duplication.
End-to-end observability
Distributed tracing, per-service RED metrics, log correlation by request ID, SLOs per service. When something breaks, you can see exactly where — without guessing.
A deploy and rollback story
GitOps deploys, canary rollouts, database-migration patterns that are safe to reverse. Every service can be rolled back independently without taking the platform down.
A Real Microservices Team
Microservices touch domain modeling, infra, and data all at once. Six roles you get on every Pillai Infotech platform build.
Solution Architect
Owns the big picture: service boundaries, sync versus async, where consistency is strict and where it's eventual. Draws the diagrams and defends them.
Domain-Driven Design Specialist
Runs event-storming sessions, maps bounded contexts, names aggregates. Makes sure the split lines up with the business, not the org chart.
Platform Engineer
Builds the shared service template, deploy pipelines, base images, secret management, config patterns. Turns "ten services" into "one platform with ten services."
Observability Lead
Designs tracing, metrics, logging, and SLOs. Wires them into on-call so the right person gets paged for the right reason.
Service Mesh Engineer
Evaluates whether a mesh is actually needed, picks the simplest option that works, tunes mTLS, traffic policies, and retries. Fights complexity on your behalf.
Data Consistency Lead
Handles the hard part: sagas, outbox patterns, idempotency, eventual consistency, CDC. Makes sure data across services stays sane without distributed transactions.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
Platforms We Ship Every Quarter
We build microservice platforms where the complexity is a deliberate trade, not an accident.
🌱 Greenfield platforms
A new product built as a microservice platform from day one — because the team size and domain genuinely justify it. Service template, mesh, observability, CI/CD all wired on day one.
🪢 Monolith strangler migrations
Extract services from a legacy monolith one bounded context at a time, behind a façade, with the old system running until the new one is proven. No big-bang rewrites.
📨 Event-driven architectures
Kafka or NATS as the spine, event schemas in a registry, outbox pattern for reliable publishing, replay for recovery. Eventual consistency on purpose, not by accident.
🎬 Saga / orchestration setups
Long-running workflows across services via Temporal or a saga orchestrator. Compensations, retries, timeouts, and visibility into every in-flight transaction.
🧰 Internal platforms
A golden path for your engineers: service template, paved CI/CD, observability defaults, secret management, cost dashboards. So the next team ships in days, not months.
🕸️ Service mesh adoption
Istio or Linkerd, introduced incrementally, with a clear answer to "what does this give us that we did not have yesterday" for every feature we turn on.
The Microservices Stack We Use
We pick the boring, well-understood tool over the shiny one — then justify every exception in writing.
Orchestration & Mesh
Messaging & Workflows
Observability
Inter-service
A Six-Stage Microservices Delivery Process
Designed to say no to the splits that will hurt and yes to the ones that will help. We spend real time on the boundaries.
Domain Discovery & Event Storming
Workshops with your product and engineering teams to map the business events, aggregates, and bounded contexts. The split lines come out of this — not out of a whiteboard guess.
Boundary & Data Design
We decide what is sync versus async, where consistency must be strict, and where it can be eventual. Every service owns its data — no shared database — and cross-service reads are designed on purpose.
Platform Foundation
Service template, CI/CD, base images, logging, tracing, auth, config, secrets. Before service number one ships, the platform the rest will ride on is working.
Build & Strangle
Services built or extracted one bounded context at a time, behind a façade where needed. The old system keeps running until the new one is measurably better.
Observe & Harden
SLOs per service, alerts wired into on-call, chaos tests on the critical paths, load tests on the top flows, runbooks for the top failure modes.
Handover & Golden Path
A documented golden path for adding new services, written runbooks, a recorded walkthrough, and a warranty window where we are on-call with your team.
Three Ways to Engage
Microservices work comes in different shapes. Pick the one that matches where you are.
Architecture Audit
Three-week review of an existing microservice platform or a monolith considering the jump. Boundaries, data, ops, observability, cost. You get a prioritized plan.
- Domain & boundary review
- Platform & ops review
- Honest go / slow-down / pause call
Platform Build or Migration
End-to-end greenfield platform or strangler migration — domain, services, platform, observability, golden path.
- Fixed-scope, fixed-price
- Typical: 12–24 weeks
- Includes 30-day production warranty
Embedded Platform Team
A dedicated platform squad working alongside your team over multiple quarters of service extraction and platform work.
- Architect + platform eng + SRE
- Monthly retainer, scale up/down
- Best for: 3+ months of roadmap
Honest Answers to Microservices Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
When should we NOT do microservices?
If you have fewer than three engineering teams, an unclear domain, or a product less than a year old — almost certainly not yet. A well-structured modular monolith will ship faster, cost less to run, and preserve every future option. We tell clients to wait more often than we tell them to split.
How small is too small for a service?
If a service cannot be owned by a team, deployed independently, and understood end-to-end, it is too small. Nanoservices create operational cost without independence. The right size is "one team, one bounded context, one database" — not "one function, one repo."
How do we handle the shared database problem?
We don't share databases across services. Each service owns its data. Where other services need that data, they consume events via Kafka/NATS or call a read API. Migrations from a shared DB use the expand-contract pattern: new service writes to its own store, old system keeps working, cutover happens after parity.
What about distributed transactions?
We avoid them. Long-running business processes are modeled as sagas — a sequence of local transactions with explicit compensations — usually via Temporal or an orchestrator service. Where strict consistency is non-negotiable, we keep those operations inside a single service on purpose.
How do you debug across services?
Distributed tracing via OpenTelemetry, request IDs propagated on every hop, logs correlated by that ID, traces visible in Jaeger or Tempo. When something is slow or broken, you see the full call graph, not ten disconnected log tails.
How many engineers do we need for this?
Rough rule: at least one team of three to five engineers per service, plus a platform team of two to four once you pass five or six services. If the numbers don't work, microservices are the wrong answer — the math does not bend.
Do we actually need a service mesh?
Usually not on day one. A mesh earns its keep around ten-plus services, when you need uniform mTLS, fine-grained traffic policies, and consistent retries across languages. Before that, language-level libraries are simpler and cheaper. We turn on mesh features one at a time, with a written reason for each.
What is the rollback strategy?
Every service deploys independently via GitOps with canary or blue-green. Database migrations use expand-contract so old and new versions can run side by side. Rollback is re-pointing the canary, not a war room. We test it in staging every release.
Can we migrate from a monolith without a freeze?
Yes — that's the whole point of the strangler pattern. The monolith keeps running, a façade routes traffic, new bounded contexts are built as services and cut over one at a time. Feature delivery to the business does not stop while the migration happens.
Who owns the platform and the services?
You do. 100%. Code, infra-as-code, service templates, runbooks, dashboards — all yours, in your repos, in your clusters. We build the golden path and then your teams walk it.